{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ggplot: (285370365)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A3GPsLS0BAAAAnEKpBwAAAAxHqQcAAAAMR6kHAAAADEepBwAAAAxHqQcAAAAMR6kHAAAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1101df310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(dict(x=range(3), y=range(3), crayon=['sunset orange', 'inchworm', 'cadet blue']))\n",
    "p = ggplot(aes(x='x', y='y', color='crayon'), data=df)\n",
    "p += geom_point(size=850)\n",
    "p + scale_color_crayon()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
